Explainable machine learning financial credit approval model for protected classes of borrowers

US11941650B2 · kind B2 · utility

Assignee

• ZestFinance, Inc. · US

Inventors

• Douglas C. Merrill · Los Angeles, US
• Michael Edward Ruberry · Los Angeles, US
• Ozan Sayin · Los Angeles, US
• Bojan Tunguz · Greencastle, US
• Lin Song · Singapore, SG
• Esfandiar Alizadeh · Santa Monica, US
• Melanie Eunique DeBruin · Twin Lakes, US
• Yachen Yan · Los Angeles, US
• Derek Wilcox · Los Angeles, US
• John Candido · Burbank, US
• Benjamin Anthony Solecki · Los Angeles, US
• Jiahuan He · Beijing, CN
• Jerome Louis Budzik · Winthrop Harbor, US
• Sean Javad Kamkar · Los Angeles, US

Key dates

• Filing date: Aug 1, 2018
• Grant date: Mar 26, 2024
• Expiry date: Jul 9, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0201
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.